Forest biomass monitoring with GNSS-R: Theoretical simulations

Ferrazzoli, P.; Guerriero, L.; Pierdicca, Nazzareno; Rahmoune, R.

doi:10.1016/j.asr.2010.04.025

Cited by 113 publications

(88 citation statements)

References 32 publications

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“…However, for the rest of estimations, the correlations are encouraging, especially for PWC, LAI and FVC. These parameters are crucial in the active microwave estimations, while masking or attenuating the signal reflected coherently and thus introducing incoherent scattering (Ferrazzoli et al, 2011). Thus, this proved relationship with the NDVI suggests a possible inclusion in the retrieval algorithms using radar or GNSS-R signals, as did, for example, in the SMAP soil moisture algorithm including the NDVI for vegetation correction, which resulted in robust and stable soil moisture retrievals (Entekhabi et al, 2010).…”

Section: Ndvi and Field Measurements Relationshipsmentioning

confidence: 99%

New Microwave-Based Missions Applications for Rainfed Crops Characterization

Sánchez

López-Sánchez

Arias-Pérez³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, WG I/2 KEY WORDS: Radar, crops, Landsat 8, Radarsat-2, GNSS-R ABSTRACT:A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsatderived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.

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Section: Ndvi and Field Measurements Relationshipsmentioning

confidence: 99%

New Microwave-Based Missions Applications for Rainfed Crops Characterization

Sánchez

López-Sánchez

Arias-Pérez³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The estimation of the quantitative impact is very difficult, being a combination of incidence angle, wavelength, biomass volume, height, and loss component induced by the dielectric constant of water-containing stalks and leaves. In addition to the theoretical approach described by Ulaby et al (see [44,45]), a detailed analysis is presented in [22,23]. As a first approximation, an average reduction of the SNR of 2 dB due to the effect of vegetation will be taken into account.…”

Section: Montoro Experiments (22 August 2013)mentioning

confidence: 99%

“…[14][15][16]), vegetation coverage (see e.g. [17][18][19][20][21][22]), and soil moisture (see e.g. [23][24][25][26]).…”

Section: Introductionmentioning

confidence: 99%

Detection of buried objects using reflected GNSS signals

Notarpietro

Mattia

Campanella

et al. 2014

EURASIP J. Adv. Signal Process.

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The use of reflected Global Navigation Satellite System (GNSS) signals for sensing the Earth has been growing rapidly in recent years. This technique is founded on the basic principle of detecting GNSS signals after they have been reflected off the Earth's surface and using them to determine the properties of the reflecting surface remotely. This is the so-called GNSS reflectometry (GNSS-R) technique. In this paper, a new application regarding the detection of metallic buried objects is analyzed and it is validated through several experimental campaigns. Although the penetration depth of GNSS signals into the ground is not optimal and depends on the soil moisture, GNSS signals can likely interact approximately with the first 10 cm of the ground and therefore can be reflected back by any metallic object buried on the first terrain layer. A very light and low-cost GNSS receiver prototype based on a software-defined radio approach was developed. This receiver can be used as a payload on board small drones or unmanned aerial systems to detect metallic objects (mines or other explosive devices). A signal processing tool based on an open-loop GNSS signal acquisition strategy was developed. The results of two experiments which show the possibility of using GNSS-R signals to detect buried metallic objects and to provide an estimate of their dimensions are discussed.

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“…In particular, the coherent electromagnetic field was modeled as the reflection of the GNSS signals over the soil, attenuated by the vegetation above it. In this work [16], it was stated that the coherent component of the co-polar reflected signal is 30 dB lower than the cross-polar one. The incoherent component is dominant for co-polar signatures and for a biomass density larger than 50 ton/ha, while the coherent component is the highest for cross-polar signals up to 200 t/ha.…”

Section: Introductionmentioning

confidence: 99%

“…Later, theoretical simulations were carried out to evaluate the performance of GNSS-R polarimetric measurements for biomass monitoring [16]. Coherent and incoherent scattering were considered in the simulations.…”

Section: Introductionmentioning

confidence: 99%

First Polarimetric GNSS-R Measurements from a Stratospheric Flight over Boreal Forests

et al. 2015

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Abstract:The first-ever dual-frequency multi-constellation Global Navigation Satellite Systems Reflectometry (GNSS-R) polarimetric measurements over boreal forests and lakes from the stratosphere are presented. Data were collected during the Swedish National Space Board (SNSB)/European Space Agency (ESA) sponsored Balloon Experiments for University Students (BEXUS) 19 stratospheric balloon experiment using the P(Y) and C/A ReflectOmeter (PYCARO) instrument operated in closed-loop mode. Maps of the polarimetric ratio for L1 and L2 Global Positioning System (GPS) and GLObal Navigation Satellite System (GLONASS), and for E1 Galileo signals are derived from the float phase at 27,000 m height, and the specular points are geolocalized on the Earth's surface. Polarimetric ratio (Γ /Γ ) maps over boreal forests are shown to be in the range 2-16 dB for the different GNSS codes. This result suggests that the scattering is taking place not only over the soil, but over the different forests elements as well. Additionally to the interpretation of the experimental results a theoretical investigation of the different contributions to the total reflectivity over boreal forests is performed using a bistatic scattering model. The simulated cross-(reflected Left Hand Circular Polarization LHCP) and co-polar (reflected Right Hand Circular Polarization RHCP) reflectivities are evaluated for the soil, the canopy, and the canopy-soil interactions for three different biomass densities: 725 trees/ha, 150 trees/ha and 72 trees/ha. For elevation angles larger than the Brewster angle, it is found that the cross-polar OPEN ACCESSRemote Sens. 2015, 7 13121 signal is dominant when just single reflections over the forests are evaluated, while in the case of multiple reflections the co-polar signal becomes the largest one.

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Forest biomass monitoring with GNSS-R: Theoretical simulations

Cited by 113 publications

References 32 publications

New Microwave-Based Missions Applications for Rainfed Crops Characterization

New Microwave-Based Missions Applications for Rainfed Crops Characterization

Detection of buried objects using reflected GNSS signals

First Polarimetric GNSS-R Measurements from a Stratospheric Flight over Boreal Forests

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